AI Moved the Bottleneck — and this time it landed in product
We ran out of stories. Our AI pipeline moved faster than product could spec new work. The bottleneck didn't disappear when we shifted left — it migrated.
We ran out of stories. Our AI pipeline moved faster than product could spec new work. The bottleneck didn't disappear when we shifted left — it migrated.
AI makes seniors faster and juniors easier to skip — right when the industry needs more seniors, not fewer. A pipeline problem, not a productivity story.
Introducing the ADLC — the Agent Development Lifecycle. The loop that governs how you calibrate AI agents to produce reliable output.
How 15 specialized agents go from ADO story to shipped PR — orchestration, context management, quality gates, and drift detection.
Author agents enforce quality structurally — rating AI output, retrying on partial passes, and escalating when something is fundamentally wrong.
Without a feedback loop, your agent skills teach stale patterns — or worse, propagate security rules that were never right.
The architecture that cut per-session agent context by 85–89% — without losing a single useful rule.
Not all agent context is the same. The difference between skills, instructions, and scripts determines whether your agent stays focused.
Using the most powerful model for everything isn't a strategy — it's expensive and slow. Here's how to match models to tasks.
A single generalist agent doing everything sounds simpler. It isn't. Here's why specialists produce better output and preserve context at handoffs.
AI isn't replacing QE — it's making the discipline more essential. The judgment behind good testing is exactly what AI can't supply.